1. Technological Field
The present disclosure relates generally to artificial neural networks, and more particularly in one exemplary aspect to computer apparatus and methods for plasticity implementation in a pulse-code neural network.
2. Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011, and U.S. patent application Ser. No. 13/152,119, Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, each incorporated herein by reference in its entirety.
Typically, artificial spiking neural networks, such as the exemplary network described in owned U.S. patent application Ser. No. 13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORK APPARATUS AND METHODS”, may comprise a plurality of units (or nodes), which can be thought of as corresponding to neurons in a biological neural network. Any given unit may be connected to many other units via connections, also referred to as communications channels, and/or synaptic connections. The units providing inputs to any given unit are commonly referred to as the pre-synaptic units, while the units receiving the inputs are referred to as the post-synaptic units.
Each of the unit-to-unit connections may be assigned, inter alia, a connection efficacy, which in general may refer to a magnitude and/or probability of input spike influence on unit output response (i.e., output spike generation/firing). The efficacy may comprise, for example a parameter—e.g., synaptic weight—by which one or more state variables of post-synaptic unit are changed. In one or more implementations, the efficacy may comprise a latency parameter by characterizing propagation delay from a pre-synaptic unit to a post-synaptic unit. In some implementations, greater efficacy may correspond to a shorter latency. During operation of a pulse-code network, neuron responses to input stimulus may be characterized by a firing rate (e.g., a number of spikes per unit time). Individual neurons of such network may be configured to respond to one or more features of the input (e.g., a vertical and/or a horizontal edge, an object of a certain color, and/or other feature). When some of the features (e.g., red circles) may be more prevalent within the input, the neurons with the receptive fields corresponding to the red circles may respond more frequently (e.g., with a higher firing rate) to the input. Such frequent responses may overwhelm or ‘drown-out’ neurons responding to less frequent/prominent features of the input, and/or lead to an excessive use of network communication, computational, and/or power resources.
Consequently, there is a salient need for improved network operation capable of, inter alia, equalizing firing adaptive plasticity mechanisms to enable a pulse-code (e.g., spiking) neuron network capable of operating in a wide variety of input and network dynamic regimes.